Low-power always-on audio environment classification may be envisioned as an enabler of context awareness applications for smartphones. While there have been attempts to facilitate supervised (i.e. pre-trained) audio classification, performance of such supervised audio classification may be mixed as the correct semantic label for a given ambient audio environment can often be user-specific. For example, the ambience of one person's home may sound like the ambience of another person's office. In addition, conventional audio classification methods can be computationally intensive, rendering such methods unsuitable for implementation in a low-power always-on manner. Another issue with conventional audio classification methods is the concern of privacy as the original audio samples may be stored.